Competing methods in the simulation study are covariance graphical lasso (GL) proposed by [1]} and sample covariance matrix (Samp). For our proposed method, we obtain MCMC samples of \(\mathcal {Z}\)  from the posterior using Algorithm REF  described in Section REF  and choose the final model either by MPM or MAP. We generate 12000 posterior samples after 3000 burn-in for our proposed method. Given the chosen model, we estimate \(\Sigma \)  by solving (17) using Algorithm REF  by the argument in Section REF . The simulation results are provided in Table REF  and Table REF . Note that for each measure, the boldfaced value denotes the best performance compared to other competing methods in each dimension (sample size) and model.
